Multi-process constrained estimation

Hintz, Kenneth J.; McVey, Eugene S.

doi:10.1109/21.101154

Cited by 75 publications

(43 citation statements)

References 19 publications

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“…In the context of Bayesian estimation, a good measure of the quality of a sensing action is the reduction in entropy of the posterior distribution that is expected to be induced by the measurement. Therefore, it may be beneficial to choose the sensing action that maximizes the expected gain in information based on a variety of information-theoretic measures [114,115,[121][122][123][124][125].…”

Section: (I) Reduced Complexity Metrics For Trackingmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Section: (I) Reduced Complexity Metrics For Trackingmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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“…Specifically, the quality of a proposed action (be it moving the sensor to another location, or pointing an antenna in a particular direction) is measured by the amount of information that is expected to be gained by its execution. This approach is related to that of others, including Zhao [15], Hintz [16], Schmaedeke [17], and others [18], [19], [20] as discussed in [14] and elsewhere. At each epoch when a decision is to be made, the uncertainty about the surveillance region (as captured by the JMPD) is used to compute the value of each of the possible sensing actions using an information theoretic measure called the Rényi (alpha-) Divergence.…”

Section: Introductionmentioning

confidence: 99%

An Information-Based Approach to Sensor Management in Large Dynamic Networks

et al. 2007

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. An effective sensor management algorithm must combine prior knowledge, sensor models, environment models, and measurements to predict the best actions to take.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.Index Terms-multiplatform sensor management, information theory, particle filtering, joint multitarget probability density, multitarget tracking.

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“…Work in [46][47][48][49][50][51][52][53] use information measures such as entropy and discrimination gain for goals such as determining resolution level of a sensor, determining priority of search and track tasks, etc. Hintz et al [9,10] use the Shannon entropy, as we do in this paper, while Schmaedeke and Kastella [11] have chosen to use Kullback-Leibler (KL) divergence as measure of information gain. Mahler [55][56][57] proposed Finite set statistics (FISST) which reformulates the problem of multi-sensor, multi-target data fusion as if it were a single-sensor, single-target tracking problem.…”

mentioning

confidence: 99%

“…The advantage of JMP is that there is a global notion of system tasks priority with all task priorities in a common space. Hintz and McVey [45] used entropy for search, track and ID tasks. Work in [46][47][48][49][50][51][52][53] use information measures such as entropy and discrimination gain for goals such as determining resolution level of a sensor, determining priority of search and track tasks, etc.…”

mentioning

confidence: 99%

Distributed sensor resource management and planning

Khosla

Guillochon

2007

SPIE Proceedings

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The goal of sensor resource management (SRM) is to allocate resources appropriately in order to gain as much information as possible about a system. In our previous paper, we introduced a centralized non-myopic planning algorithm, C-SPLAN, that uses sparse sampling to estimate the value of resource assignments. Sparse sampling is related to Monte Carlo simulation. In the SRM problem we consider, our network of sensors observes a set of tracks; each sensor can be set to operate in one of several modes and/or viewing geometries. Each mode incurs a different cost and provides different information about the tracks. Each track has a kinematic state and is of a certain class; the sensors can observe either or both of these, depending on their mode of operation. The goal is to maximize the overall rate of information gain, i.e. rate of improvement in kinematic tracking and classification accuracy of all tracks in the Area of Interest. We compared C-SPLAN's performance on several tracking and target identification problems to that of other algorithms. In this paper we extend our approach to a distributed framework and present the D-SPLAN algorithm. We compare the performance as well as computational and communications costs of C-SPLAN and D-SPLAN as well as near-term planners.

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Multi-process constrained estimation

Cited by 75 publications

References 19 publications

Distributed inference in wireless sensor networks

Distributed inference in wireless sensor networks

An Information-Based Approach to Sensor Management in Large Dynamic Networks

Distributed sensor resource management and planning

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